DESCRIPTION (investigator's abstract): The fundamental goals of the Mapping project are (1) to continue to develop and evaluate a scientifically compelling taxonomy of individual differences, one that is widely generalizable across different taxonomic procedures and different languages and cultures, and (2) to improve the methodologies for measuring these individual differences, through collaboration with a world-wide network of other scientists. Supported by a MERIT award from NIMH over the past seven years, we have been instrumental in extending the scope of the "Big-Five" factor structure of phenotypic personality attributes, and comparing it with other lexically derived models of personality structure. In the years ahead, we intend to further test the Big Five against several specific competitor models, and to examine the effects of methodological variations and kinds of populations on the resulting structures. In addition, we will clarify the middle-level facets of many of these models, and test their generality across a variety of the world's languages. Among the resources that we have developed for these purposes, the most important are (a) a large community sample of highly motivated research participants who have worked with us over the past seven years and have agreed to continue working with us over the next five to ten years, (b) a large adult sample in Hawaii, all of whom were assessed by their elementary-school teachers approximately 40 years ago, and (c) a team of linguistic consultants, with whom we are recruiting additional participant samples. As part of this effort, we will expand the focus of our research beyond personality traits to include social attitudes and implicit motives as derived from fantasy productions. In so doing, we intend to position personality variables at the hub of a developing model that includes a wide collection of important individual differences. We search for a structural model that is strong in at least three distinct ways. First, it is to be replicable across different types of data and samples, including different languages and cultures. Second, the model is to be comprehensive, with few important variables falling outside of the structure. And third, measures of the components in the model should have high utility, functioning as a powerful predictive resource embedded in a rich and informative nomological network.